Three Tips for Making Network Analysis Actionable for Your Social Impact Project

Many of our partners have adopted what Jed Miller and Rob Stuart called “Network-Centric Thinking.” They recognize that long-term sustainable progress on today’s social problems rarely comes from the efforts of a single organization. Rather, progress requires a strategy involving networks of organizations with the aim of producing network effects.

However, the strategist and evaluator’s task of connecting network strategy to network effects to final outcomes is often difficult, not least because networks are embedded in complex, adaptive systems in which cause and effect relationships are rarely straightforward. Moreover, because quantitative social network analysis (SNA) is often new to many social impact organizations, it is easy to get bogged down in superficial findings to the determinant of more actionable insights.

There are now a large number of resources on designing network analyses for complex evaluations (see some of our favorites below), but we’ve found three tips particularly useful for ensuring a network analysis yields actionable insights. In short, a design for evaluating a network should:

Start by adopting a framework for how network structure leads to network effects;

Avoid the lure of only using quantitative SNA; and

Design your network analysis with future data collections in mind: connecting change in the network to outcomes is one of the most powerful insights you’ll uncover.

Get a Framework

Our partners often make use of theories of change, systems maps, scenario mapping, power analyses, and other tools to frame the nature of the problem they want to address and to develop strategies to guide their work. For learning partners like us, these tools are often a key part of developing and shaping evaluation questions and hypotheses. However, because network theory is relatively new to most people, the expected impact of network strategies is often underspecified in these documents.

For example:

An initiative may agree that the presence of working relationships among cross-sector partners is an important interim outcome…

…with the expectation these partnerships will help address an upstream driver of a problem…

…but they may not fully consider how the strengths and weaknesses of the current network structure alters
their chances of activating this “network effect”…

…which in turn limits their understanding of which actions are needed to advance the network strategy.

Frameworks help to address these problems because they relate network structure to network effects. For example, Peter Plastrik and Madeleine Taylor describe three networks [pdf] on the basis of the depth of their connections – connectivity, alignment, and production. If an initiative aims for cross-sector collaboration (production), but the initial network analysis reveals little connectivity between organizations, it’s best to engage in more connectivity-related and alignment-related network building tasks before encouraging project collaboration.

Choose a Multi-method Approach

When most people think about network analysis, they think of network maps or strange-sounding network statistics like density or centrality. This is quantitative SNA, and it is an essential tool for describing structural properties of a network. Among other things, an SNA will reveal gaps in the network (e.g. perhaps organizations from a certain sector are underrepresented), show areas of deep or shallow connections (e.g connectivity among one subset and alignment among another subset), and identify which organizations play important roles in the network (e.g. bring unique partners to the network).

However, if used alone, SNA may mask a lot of the network information leaders need to make effective decisions. For example, network strategy often involves developing structures for coordination, including convenings, working groups, and shared measurement systems. While it’s possible to use SNA to wrangle some insights about whether these coordinating efforts lead to more effective partnerships, it’s often more meaningful to hear from participants how these structures influenced their work. In short, interviews are much better at capturing the organizational and inter-organizational effects of the network – innovations, greater efficiencies realized, knowledge and information shared, etc.

Design with the Future in Mind

It is good practice to design any evaluation with pre- and post-interventions in mind. Especially for quantitative SNA, it is worth the upfront time to identify what you hope your network will look like in the future, not just examine it today. Repeated network maps can show how the network is evolving over time, which is a great way to identify how coordinating efforts are producing network-level effects (e.g., better representation of certain sectors at convening events, connections made between subsets of the networks, etc.). Again, adopting a framework can be very useful. Many frameworks explicitly describe the stages of network evolution and provide guidance on how to identify and manage a network in transition.

The more social change agents adopt network-centric thinking, the better the chances we’ll make real progress on today’s social problems. We can support this mindset by ensuring our network analyses produce actionable insights. We’ve found these three tips are useful to our work. Based on your experiences, what other tips do you recommend?

New to network thinking or network analysis? Here’s a few of our favorite resources.